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image of Two-Stage Multi-View Graph Spectral Clustering for Single-Cell RNA-Seq Data

Abstract

Introduction

The appearance of single-cell RNA sequencing (scRNA-seq) data has brought a distinctive perspective to studying gene expression at the cell level. However, it faces challenges such as large data volume, sparsity, heterogeneity, and the curse of dimensionality. Current clustering methods still face many challenges in studying cell type distribution and have not utilized the structural relationship information between cells.

Methods

To avoid the insufficiency of the single characteristic space of scRNA-seq data in characterizing cell function, this paper constructs multiple view characteristic spaces and utilizes multi-view learning to characterize scRNA-seq information from distinctive perspectives comprehensively. In multi-view learning, the similarity graph is divided into weighted learning and structural learning stages. Through weighting the multi-view similarity graphs, the significance of diverse views and features is underscored. During the structural stage, the emphasis is placed on uncovering potential relationships among different views by preserving common edges in the multi-view similarity graphs. The optimization of the attribute and structure graphs was conducted separately by the alternating direction multiplier method.

Results

The performance of the MVGSC was validated using eight different scales of real scRNA-seq datasets, and the experimental results showed that the proposed multi-view clustering method significantly surpasses other single-view clustering methods and multi-view clustering methods.

Discussion

When the features of scRNA-seq data are complex and there are significant differences between views, the two-stage multi-view graph method can better capture the complex relationships in the data, demonstrating superior performance compared to a single framework.

Conclusion

Two-stage multi-view learning can more accurately capture complex relationships in the data, thereby improving the accuracy of the model. It can also better capture consistency and complementary information in multi-view data, thereby enhancing the generalization ability of the model.

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2025-10-10
2025-12-04

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References

  1. Wu W. Ma X. Joint learning dimension reduction and clustering of single-cell RNA-sequencing data. Bioinformatics 2020 36 12 3825 3832 10.1093/bioinformatics/btaa231 32246821
    [Google Scholar]
  2. Wan H. Chen L. Deng M. scNAME: neighborhood contrastive clustering with ancillary mask estimation for scRNA-seq data. Bioinformatics 2022 38 6 1575 1583 10.1093/bioinformatics/btac011 34999761
    [Google Scholar]
  3. Wang Y. Wong K.C. Li X. Exploring high-throughput biomolecular data with multiobjective robust continuous clustering. Inf. Sci. 2022 583 239 265 [J]. 10.1016/j.ins.2021.11.030
    [Google Scholar]
  4. Yu L. Cao Y. Yang J.Y.H. Yang P. Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data. Genome Biol. 2022 23 1 49 10.1186/s13059‑022‑02622‑0 35135612
    [Google Scholar]
  5. Grün D. Lyubimova A. Kester L. Wiebrands K. Basak O. Sasaki N. Clevers H. van Oudenaarden A. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 2015 525 7568 251 255 10.1038/nature14966 26287467
    [Google Scholar]
  6. Zheng R. Li M. Liang Z. Wu F.X. Pan Y. Wang J. SinNLRR: a robust subspace clustering method for cell type detection by non-negative and low-rank representation. Bioinformatics 2019 35 19 3642 3650 10.1093/bioinformatics/btz139 30821315
    [Google Scholar]
  7. Wang B. Zhu J. Pierson E. Ramazzotti D. Batzoglou S. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat. Methods 2017 14 4 414 416 10.1038/nmeth.4207 28263960
    [Google Scholar]
  8. Ng A. Jordan M. Weiss Y. On spectral clustering: Analysis and an algorithm. Adv. Neural Inf. Process. Syst. 2001 ••• 14 [J].
    [Google Scholar]
  9. Wang H. Yang Y. Liu B. GMC: Graph-based multi-view clustering. IEEE Trans. Knowl. Data Eng. 2020 32 6 1116 1129 [J]. 10.1109/TKDE.2019.2903810
    [Google Scholar]
  10. Peng X. Huang Z. Lv J. Zhu H. Zhou J.T. COMIC: Multi-view clustering without parameter selection. Proceedings of the 36th International Conference on Machine Learning (ICML) Long Beach, California, USA, 2019, pp. 5092–5101. 10.48550/arXiv.1904.01428
    [Google Scholar]
  11. Chen M.S. Huang L. Wang C.D. Huang D. Multi-view clustering in latent embedding space. Proc. Conf. AAAI Artif. Intell. 2020 34 4 3513 3520 [C]. 10.1609/aaai.v34i04.5756
    [Google Scholar]
  12. Lin Y. Zou G. Clustering single-cell RNA sequencing data by multi-view latent embedding learning. Proceedings of the 2021 International Conference on Computer Information Science and Artificial Intelligence (CISAI) China, 2021, pp. 797–801. 10.1109/CISAI54367.2021.00160
    [Google Scholar]
  13. Wu W. Zhang W. Hou W. Ma X. Multi-view clustering with graph learning for scRNA-seq data. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2023 20 6 3535 3546 10.1109/TCBB.2023.3298334 37486829
    [Google Scholar]
  14. Zeng P. Lin Z. scICML: Information-theoretic co-clustering-based multi-view learning for the integrative analysis of single-cell multi-omics data. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2024 21 1 200 207 10.1109/TCBB.2023.3305989 37590102
    [Google Scholar]
  15. Qi R. Wu J. Guo F. Xu L. Zou Q. A spectral clustering with self-weighted multiple kernel learning method for single-cell RNA-seq data. Brief. Bioinform. 2021 22 4 bbaa216 10.1093/bib/bbaa216 33003206
    [Google Scholar]
  16. Liu B.Y. Huang L. Wang C.D. Fan S. Yu P.S. Adaptively weighted multiview proximity learning for clustering. IEEE Trans. Cybern. 2021 51 3 1571 1585 10.1109/TCYB.2019.2955388 31841432
    [Google Scholar]
  17. HE T Adaptive learning-based multi-view unsupervised feature selection method. Jisuanji Yingyong 2023 43 9 2657
    [Google Scholar]
  18. Yan W. Zhang Y. Lv C. Tang C. Yue G. Liao L. Lin W. GCFAgg: Global and cross-view feature aggregation for multi-view clustering. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Vancouver, Canada, 2023, pp. 19863–19872. 10.1109/CVPR52729.2023.01902
    [Google Scholar]
  19. Wang Y. Chang D. Fu Z. Wen J. Zhao Y. Incomplete multiview clustering via cross-view relation transfer. IEEE Trans. Circ. Syst. Video Tech. 2023 33 1 367 378 [J]. 10.1109/TCSVT.2022.3201822
    [Google Scholar]
  20. Li Z. Tang C. Chen J. Wan C. Yan W. Liu X. Diversity and consistency learning guided spectral embedding for multi-view clustering. Neurocomputing 2019 370 128 139 [J]. 10.1016/j.neucom.2019.08.002
    [Google Scholar]
  21. Li Z. Tang C. Liu X. Zheng X. Zhang W. Zhu E. Consensus graph learning for multi-view clustering. IEEE Trans. Multimed. 2022 24 2461 2472 [J]. 10.1109/TMM.2021.3081930
    [Google Scholar]
  22. Zhan K. Niu C. Chen C. Nie F. Zhang C. Yang Y. Graph structure fusion for multiview clustering. IEEE Trans. Knowl. Data Eng. 2019 31 10 1984 1993 [J]. 10.1109/TKDE.2018.2872061
    [Google Scholar]
  23. Steinbach M. Tan P.N. kNN: k-nearest neighbors. The Top Ten Algorithms in Data Mining Chapman and Hall/CRC 2009 12
    [Google Scholar]
  24. Zhang S. Li X. Zong M. Zhu X. Wang R. Efficient kNN classification with different numbers of nearest neighbors. IEEE Trans. Neural Netw. Learn. Syst. 2018 29 5 1774 1785 10.1109/TNNLS.2017.2673241 28422666
    [Google Scholar]
  25. Zhang H. Kiranyaz S. Gabbouj M. Data clustering based on community structure in mutual k-nearest neighbor graph. 41st International Conference on Telecommunications and Signal Processing (TSP) Athens, Greece, 2018, pp. 1-7 10.1109/TSP.2018.8441226
    [Google Scholar]
  26. Fu L. Lin P. Vasilakos A.V. Wang S. An overview of recent multi-view clustering. Neurocomputing 2020 402 148 161 10.1016/j.neucom.2020.02.104
    [Google Scholar]
  27. Fang U. Li M. Li J. Gao L. Jia T. Zhang Y. A comprehensive survey on multi-view clustering. IEEE Trans. Knowl. Data Eng. 2023 35 12 12350 12368 [J]. 10.1109/TKDE.2023.3270311
    [Google Scholar]
  28. Tang C. Li Z. Wang J. Liu X. Zhang W. Zhu E. Unified one-step multi-view spectral clustering. IEEE Trans. Knowl. Data Eng. 2023 35 6 6449 6460 [J]. 10.1109/TKDE.2022.3172687
    [Google Scholar]
  29. Wu J. Lin Z. Zha H. Essential tensor learning for multi-view spectral clustering. IEEE Trans. Image Process. 2019 28 12 5910 5922 10.1109/TIP.2019.2916740 31217104
    [Google Scholar]
  30. Huang D. Wang C.D. Wu J.S. Lai J-H. Kwoh C-K. Ultra-scalable spectral clustering and ensemble clustering. IEEE Trans. Knowl. Data Eng. 2020 32 6 1212 1226 [J]. 10.1109/TKDE.2019.2903410
    [Google Scholar]
  31. Usoskin D. Furlan A. Islam S. Abdo H. Lönnerberg P. Lou D. Hjerling-Leffler J. Haeggström J. Kharchenko O. Kharchenko P.V. Linnarsson S. Ernfors P. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat. Neurosci. 2015 18 1 145 153 10.1038/nn.3881 25420068
    [Google Scholar]
  32. Engel I. Seumois G. Chavez L. Samaniego-Castruita D. White B. Chawla A. Mock D. Vijayanand P. Kronenberg M. Innate-like functions of natural killer T cell subsets result from highly divergent gene programs. Nat. Immunol. 2016 17 6 728 739 10.1038/ni.3437 27089380
    [Google Scholar]
  33. Pollen A.A. Nowakowski T.J. Shuga J. Wang X. Leyrat A.A. Lui J.H. Li N. Szpankowski L. Fowler B. Chen P. Ramalingam N. Sun G. Thu M. Norris M. Lebofsky R. Toppani D. Kemp D.W. II Wong M. Clerkson B. Jones B.N. Wu S. Knutsson L. Alvarado B. Wang J. Weaver L.S. May A.P. Jones R.C. Unger M.A. Kriegstein A.R. West J.A.A. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 2014 32 10 1053 1058 10.1038/nbt.2967 25086649
    [Google Scholar]
  34. Darmanis S. Sloan S.A. Zhang Y. Enge M. Caneda C. Shuer L.M. Hayden Gephart M.G. Barres B.A. Quake S.R. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl. Acad. Sci. USA 2015 112 23 7285 7290 10.1073/pnas.1507125112 26060301
    [Google Scholar]
  35. Grover A. Sanjuan-Pla A. Thongjuea S. Carrelha J. Giustacchini A. Gambardella A. Macaulay I. Mancini E. Luis T.C. Mead A. Jacobsen S.E.W. Nerlov C. Single-cell RNA sequencing reveals molecular and functional platelet bias of aged haematopoietic stem cells. Nat. Commun. 2016 7 1 11075 10.1038/ncomms11075 27009448
    [Google Scholar]
  36. Goolam M. Scialdone A. Graham S.J.L. Macaulay I.C. Jedrusik A. Hupalowska A. Voet T. Marioni J.C. Zernicka-Goetz M. Heterogeneity in Oct4 and Sox2 targets biases cell fate in 4-cell mouse embryos. Cell 2016 165 1 61 74 10.1016/j.cell.2016.01.047 27015307
    [Google Scholar]
  37. Zeisel A. Muñoz-Manchado A.B. Codeluppi S. Lönnerberg P. La Manno G. Juréus A. Marques S. Munguba H. He L. Betsholtz C. Rolny C. Castelo-Branco G. Hjerling-Leffler J. Linnarsson S. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 2015 347 6226 1138 1142 10.1126/science.aaa1934 25700174
    [Google Scholar]
  38. Yan L. Yang M. Guo H. Yang L. Wu J. Li R. Liu P. Lian Y. Zheng X. Yan J. Huang J. Li M. Wu X. Wen L. Lao K. Li R. Qiao J. Tang F. Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat. Struct. Mol. Biol. 2013 20 9 1131 1139 10.1038/nsmb.2660 23934149
    [Google Scholar]
  39. Yeung K.Y. Ruzzo W.L. Principal component analysis for clustering gene expression data. Bioinformatics 2001 17 9 763 774 10.1093/bioinformatics/17.9.763 11590094
    [Google Scholar]
  40. Zhang S. Wong H.S. Shen Y. Generalized adjusted rand indices for cluster ensembles. Pattern Recognit. 2012 45 6 2214 2226 [J]. 10.1016/j.patcog.2011.11.017
    [Google Scholar]
  41. McDaid A.F. Greene D. Hurley N. Normalized mutual information to evaluate overlapping community finding algorithms. arXiv 2011 1 10.48550/arXiv.1110.2515
    [Google Scholar]
  42. Amelio A. Pizzuti C. Correction for closeness: Adjusting normalized mutual information measure for clustering comparison. Comput. Intell. 2017 33 3 579 601 [J]. 10.1111/coin.12100
    [Google Scholar]
  43. Gómez-Adorno H Aleman Y Ayala D V Sanchez-Perez M.A. Author clustering using hierarchical clustering analysis. Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum Dublin, Ireland, September 11-14, 2017 2017
    [Google Scholar]
  44. Song C. Liu F. Huang Y. Auto-encoder based data clustering. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. 18th Iberoamerican Congress, CIARP 2013 Havana, Cuba, November 20-23, 2013, pp. 117-124
    [Google Scholar]
  45. Li H. Li H. Wei K. Automatic fast double KNN classification algorithm based on ACC and hierarchical clustering for big data. Int. J. Commun. Syst. 2018 31 16 e3488 10.1002/dac.3488
    [Google Scholar]
  46. Huang P. Huang Y. Wang W. Wang L. Deep embedding network for clustering. 22nd International Conference on Pattern Recognition (ICPR) Stockholm, Sweden, 2014, pp. 1532-1537 10.1109/ICPR.2014.272
    [Google Scholar]
  47. Kesuma Dinata R. Retno S. Hasdyna N. Minimization of the number of iterations in K-medoids clustering with purity algorithm. Rev Intell Artif 2021 35 3 193 199 10.18280/ria.350302
    [Google Scholar]
  48. Marutho D. Handaka S.H. Wijaya E. The determination of cluster number at k-mean using elbow method and purity evaluation on headline news. 2018 International Seminar on Application for Technology of Information and Communication Semarang, Indonesia, 2018, pp. 533-538 10.1109/ISEMANTIC.2018.8549751
    [Google Scholar]
  49. Lingras P. Chen M. Miao D. Precision of rough set clustering. rough sets and current trends in computing. 6th International Conference, OH, USA, October 23-25, 2008, pp. 369-378
    [Google Scholar]
  50. Gui-Fen C. Li-Ying C. Guo-Wei W. Bao-Cheng W. Da-You L. Sheng-Sheng W. Application of a spatial fuzzy clustering algorithm in precision fertilisation. N. Z. J. Agric. Res. 2007 50 5 1249 1254 10.1080/00288230709510409
    [Google Scholar]
  51. Van der Maaten L. Hinton G. Visualizing data using t-SNE. J. Mach. Learn. Res. 2008 9 11 [J].
    [Google Scholar]
  52. Wold S. Esbensen K. Geladi P. Principal component analysis. Chemom. Intell. Lab. Syst. 1987 2 1-3 37 52 [J]. 10.1016/0169‑7439(87)80084‑9
    [Google Scholar]
  53. Lee D.D. Seung H.S. Learning the parts of objects by non-negative matrix factorization. Nature 1999 401 6755 788 791 10.1038/44565 10548103
    [Google Scholar]
  54. Huang D. Wang C.D. Lai J.H. Fast multi-view clustering via ensembles: Towards scalability, superiority, and simplicity. IEEE Trans. Knowl. Data Eng. 2023 35 11 11388 11402 [J]. 10.1109/TKDE.2023.3236698
    [Google Scholar]
  55. Zhan K. Nie F. Wang J. Yang Y. Multiview consensus graph clustering. IEEE Trans. Image Process. 2019 28 3 1261 1270 10.1109/TIP.2018.2877335 30346283
    [Google Scholar]
  56. Zhan K. Zhang C. Guan J. Wang J. Graph learning for multiview clustering. IEEE Trans. Cybern. 2018 48 10 2887 2895 10.1109/TCYB.2017.2751646 28961135
    [Google Scholar]
  57. Roux S. Buis S. Lafolie F. Lamboni M. Cluster-based GSA: Global sensitivity analysis of models with temporal or spatial outputs using clustering. Environ. Model. Softw. 2021 140 105046 [J]. 10.1016/j.envsoft.2021.105046
    [Google Scholar]
  58. Wang L. Xu Y.P. Xu J. Gu H. Bai Z. Zhou P. Yu H. Guo Y. Increasing parameter identifiability through clustered time-varying sensitivity analysis. Environ. Model. Softw. 2024 181 106189 [J]. 10.1016/j.envsoft.2024.106189
    [Google Scholar]
  59. Dai Z. Scott M.J. Product platform design through sensitivity analysis and cluster analysis. J. Intell. Manuf. 2007 18 1 97 113 [J]. 10.1007/s10845‑007‑0011‑2
    [Google Scholar]
  60. Krzak M. Raykov Y. Boukouvalas A. Cutillo L. Angelini C. Benchmark and parameter sensitivity analysis of single-cell RNA sequencing clustering methods. Front. Genet. 2019 10 1253 10.3389/fgene.2019.01253 31921297
    [Google Scholar]
  61. Kumar A. Singh D. Yadav R.S. Entropy and improved k‐nearest neighbor search based under‐sampling (ENU) method to handle class overlap in imbalanced datasets. Concurr. Comput. 2024 36 2 e7894 [J]. 10.1002/cpe.7894
    [Google Scholar]
  62. Cottrell S. Hozumi Y. Wei G.W. K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis. Comput. Biol. Med. 2024 175 108497 10.1016/j.compbiomed.2024.108497 38678944
    [Google Scholar]
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Two-Stage Multi-View Graph Spectral Clustering for Single-Cell RNA-Seq Data
Bentham Science Publishers ; https://doi.org/10.2174/0113892029387405250921211549
/content/journals/cg/10.2174/0113892029387405250921211549
/content/journals/cg/10.2174/0113892029387405250921211549
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  • Article Type:     Research Article
Keywords: multi-view feature and structure network ; spectral clustering ; clustering algorithm ; multi-view graph ; clustering analysis ; scRNA-seq

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